SFB CREATOR receives second funding phase: Research on the future of electric machines continues
2025/12/10
With the approval of the second funding phase for the Collaborative Research Center (SFB) Transregio TRR361/F90 CREATOR, TU Darmstadt, TU Graz, JKU Linz, and the Austrian Academy of Sciences secure over 20 million euros in funding. In the next phase, the focus will be on thermal and multiphysical effects, as well as data-driven methods for modeling and optimizing the next generation of electric machines.
We are pleased to share that the German and Austrian research agencies DFG and FWF have jointly approved the second funding period of the Collaborative Research Center (CRC) Transregio TRR 361/F90 CREATOR, with total funding for both periods exceeding 20 million euros. The project brings together researchers from TU Darmstadt, TU Graz, JKU Linz, and the Austrian Academy of Sciences and continues to focus on the modeling and computational foundations of next-generation electric machine design.
During the first funding period, the consortium established an innovative framework for simulation and design. This included advances in spline-based discretizations and geometry handling, structure-preserving formulations, new mathematical models for hysteresis, and the first generation of validated demonstrators that link detailed numerical simulations with laboratory experiments.
The second funding period shifts the focus in several important ways. The most visible change is the broader treatment of thermal and multiphysical effects. In addition to electromagnetic fields and mechanics, the project will now integrate models for heat transport, multi-phase cooling, and the interaction between temperature, losses, and material behavior.
A second major development is the move towards fully three-dimensional geometry descriptions. The project will build on the spline technology developed in phase one to treat complex machine topologies directly in three dimensions and to improve the handling of moving parts. Finally, data-driven and learning-based components will play a stronger role.
The project will combine classical numerical analysis with structure-preserving surrogate models as well as methods for parameter identification and uncertainty quantification. With the support of DFG and FWF, the CRC can continue its interdisciplinary research on modeling, simulation, and optimization of electric machines.
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